Comparing Between Best Energy Efficient Techniques Worldwide with Existing Solution Implemented in Al-Ahliyya Amman University
Bibliographic record
Abstract
In this research paper, an overview of energy reduction methods around the world in commercial buildings was investigated, to find out the best solution for minimizing electricity demand of Al-Ahliyya Amman University (AAU).Those methods are renewable energy technologies mainly wind and photovoltaics (PV) system using either conventional or Carbon Nanotubes panels, building envelope system mainly dynamic Insulation materials and cool roof coating and National country system mainly demand response program and energy consumption ration. According to the overview, the best-promised method solution to achieve the target of saving, reducing investment cost and carbon emissions in AAUis the Standalone Hybrid Carbon Nanotubes PV system. This method was compared with the existing on-grid PV system project applied in AAU. The comparison was based on investment cost, payback period and solar cell efficiency. The comparison analysis results revealed that Standalone Carbon Nanotube PV system was able to save 21.12% of the investment cost when compared to the existing AAU project with a reduced payback period from 10 to 8 years and Internal Rate of Return (IRR) of 16%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".